11 May 2021

Introduction

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  • The COVID-19 pandemic is caused by the highly infectious virus; SARS-CoV-2.
  • CD8+ T cells perform essential functions in the control and elimination of viral infections
  • Here, we analyze the immunophenotypes of CD8 T cells that can recognize peptides from the SARS-CoV-2 genome
  • We compare COVID-19 patients with healthy donors (HD) never exposed to the virus.
  • Furthermore, we compare the phenotypes to the severity of the disease.
  • We also compare the phenotype of the SARS-CoV-2 specific CD8 T cells with T cells that recognize common viruses.

Materials and methods

Phenotype data

  • Phenotyping of immune cells provides imformation abouts the activation status of the cells
  • This is obtained through antibody staining and flow cytometry, which transforms a fluorescent signal into a voltage.
  • Cell populations of specific phenotype is gated in FlowJo software, and frequencies of positive cells can afterwards exported as an .xlsx file

Raw data

  • The dataset is obtained from S. K. Saini et al., Sci. Immunol. (2021)
  • =======
  • Introduction
  • Materials - Dataset
  • Method
  • Results
  • Discussion

INTRODUCTION

-The project utilizes data of immuno phenotyping analysis on Covid patient samples (flow cytometry data), which are then plotted to make analysis based on the changes in cell-surface markers expressed when the patient is infected with SARS-Cov2. -The data used was generated at DTU Healthtech - Department of Experimental and translational immunology. -It is then proceeded to wrangling of data obtained from analysis of patient samples on the onset of Covid symptoms. -The wrangled data is then plotted to compare the cell surface markers between different patients to understand the changes of the immune cells during the phase of Covid. -Here we compare the immune profiles of patients who were hospitalized and throse who were not hospitalized with those of healthy individuals

Materials and methods - Data

  • The dataset (.csv) used in the project is a rawdata extracted from .fcs file format (obtained from flowcytometry analysis)
  • Wrangling of data - Performed to clean data required for plotting
  • >>>>>>> 69f537b56691f5a81559310fc4a09dbaf793ea64
## # A tibble: 82 x 25
##    SampleID  Hospital_status `lymphocytes/sin… `lymphocytes/si… `lymphocytes/si…
##    <chr>     <chr>           <chr>             <chr>            <chr>           
##  1 Patient_1 Hospitalized    28.7              20.399999999999… 1.66            
##  2 Patient_2 Hospitalized    12.5              84.1             44.9            
##  3 Patient_3 Hospitalized    27.3              91.2             16.7            
##  4 Patient_4 Hospitalized    5.9               23.3             8.4600000000000…
##  5 Patient_5 Hospitalized    16.8              40               11.2            
##  6 Patient_6 Hospitalized    4.83              49.2             25.9            
##  7 Patient_7 NA              NA                76.7             32.1            
##  8 Patient_8 Hospitalized    1.94              45.6             5.41            
##  9 Patient_9 Hospitalized    3.76              34.1             6.15            
## 10 Patient_… Hospitalized    1.69              31.5             12              
## # … with 72 more rows, and 20 more variables:
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD69 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/PD-1 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/HLA-DR <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD27 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD57 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA-_CCR7- <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7- <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD45RA+_CCR7+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD39+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_CD69+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_HLA-DR+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/SARS_multimer+/CD38+_PD-1+ <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD38 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD39 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD69 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/PD-1 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/HLA-DR <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD27 <chr>,
## #   lymphocytes/singlet/live_cells/CD3+/CD8+/CEF_multimer+/CD57 <chr>

Data wrangling

Tidying the data set

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  • Change NA values
  • Add cohort group as a new variable
  • Collapse multiple columns into two columns: Gating and Fraction
  • Splitting the gating path into two new columns: Last and parent population
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  • Remove NA
  • Add cohort names as columns using mutate
  • Consolidate the markers on sampleID using pivot longer
  • Splitting the gating path into two columns - Last and parent population using str_detect
  • Data- Augmenting

    >>>>>>> 69f537b56691f5a81559310fc4a09dbaf793ea64
    ## # A tibble: 1,886 x 8
    ##    SampleID  cohort_type Hospital_status Gating          T_cell Parent_populati…
    ##    <chr>     <chr>       <chr>           <chr>           <chr>  <chr>           
    ##  1 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    CD8+            
    ##  2 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  3 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  4 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  5 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  6 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  7 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  8 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ##  9 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ## 10 Patient_1 Patient     Hospitalized    lymphocytes/si… CD8    SARS_multimer+  
    ## # … with 1,876 more rows, and 2 more variables: Last_population <chr>,
    ## #   Fraction <dbl>

    Creating plots

    • Reading covid_data_augment
    • <<<<<<< HEAD
    • Filter on Parent and Last population
    • Generate plot using ggplot - Boxplot and dotplot
    • Multiple plots viewed as facetwrap
    • Used geomsigif with Kruskal test

    FCS files

    Results

    Figure A

    Figure B

    Figure C

    Conclusion

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  • Filterrig on Parent and Last population
  • Generate plot using ggplot - Boxplot and dotplot
  • Multiple plots viewed as facetwrap
  • Used geomsignif with Kruskal test
  • Results

    Figures - A and B

    Figures - C and E

    Figures - D and F

    Figures - S7B and S8C

    DISCUSSION

    >>>>>>> 69f537b56691f5a81559310fc4a09dbaf793ea64